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001049195 005__ 20251211202159.0
001049195 0247_ $$2doi$$a10.1109/IECON58223.2025.11221137
001049195 037__ $$aFZJ-2025-05277
001049195 1001_ $$0P:(DE-Juel1)185033$$aZimmer, Marcel$$b0$$eCorresponding author$$ufzj
001049195 1112_ $$aIECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society$$cMadrid$$d2025-10-14 - 2025-10-17$$wSpain
001049195 245__ $$aPower Electronics Parameter Estimation by Physics-Informed Gaussian Processes
001049195 260__ $$bIEEE$$c2025
001049195 300__ $$a1-6
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001049195 520__ $$aNon-invasive parameter estimation of power electronics enables condition and health monitoring, enhances control strategies, and thus, guarantees reliable and stable operation of power converters. In this work, we propose the application of Physics-Informed Gaussian Processes (PIGP) for parameter estimation of power electronic converters. We provide a detailed model-building scheme allowing non-invasive characterisation of power converters. In particular, we show that the proposed approach can be applied independently of the particular noise level without the need for data pre-processing. Tested with a DC-DC buck converter in a simulation scenario, we show that the proposed approach is immune to measurement noise and can be executed with low sampling rates. This allows fast, possibly online, execution and thus tracking of converter parameter changes due to thermal effects or aging during operation.
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001049195 7001_ $$0P:(DE-Juel1)201161$$aDe Din, Edoardo$$b1$$ufzj
001049195 7001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b2$$ufzj
001049195 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b3$$ufzj
001049195 773__ $$a10.1109/IECON58223.2025.11221137
001049195 8564_ $$uhttps://ieeexplore.ieee.org/document/11221137
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